{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **2: Network Testing**\n",
    "\n",
    "This tutorial demonstrates how to test the double LSTM-based model for the sequence-to-scalar future hysteresis step prediction. The test set seqeunces include data that has the same sampling time steps as the training dataset. However the code is able to decipher the sequence length (provided that it has a sampling frequency), and upsample or downsample as needed to fit the model criteria. If the sequence is longer than the designed memory, then only the most recent time steps (amount to the total training memory time) are taken. If the data length is shorter, then the extra empty memory will take the constant of the most distant memory data point. The test set consists of multiple frequencies (7), temperatures (3), and 5 different combinations of past and future lengths, totaling 105 sequences, each with 1,000 time steps.\n",
    "\n",
    "# **Step 0: Import Packages**\n",
    "\n",
    "In this step we import the important packages that are necessary for the testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 34688,
     "status": "ok",
     "timestamp": 1741043766952,
     "user": {
      "displayName": "Shukai Wang",
      "userId": "00237405294920314802"
     },
     "user_tz": 300
    },
    "id": "oE_3CSIzfC1d",
    "outputId": "3cbc82eb-f205-4222-8071-ae37b22283f3"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import Tensor\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import random\n",
    "import numpy as np\n",
    "import json\n",
    "import h5py\n",
    "import math\n",
    "import csv\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ENIjgL-si0I2"
   },
   "source": [
    "# **Step 1: Define Network Structure**\n",
    "The structure of the duel LSTM-based encoder-projector-decoder neural network are defined here. The network structure does not change from the training structure. Refer to the PyTorch document for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "dYMQPMknLUPN"
   },
   "outputs": [],
   "source": [
    "# Define model structures and functions\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        self.lstm_B = nn.LSTM(1, 12, num_layers=1, batch_first=True, bidirectional=False)\n",
    "\n",
    "        self.lstm_H = nn.LSTM(1, 12, num_layers=1, batch_first=True, bidirectional=False)\n",
    "\n",
    "        self.projector = nn.Sequential(\n",
    "            nn.Linear(12 * 2 + 2 , 12 * 2 + 2),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(12 * 2 + 2, 8),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(8, 4),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(4, 1)\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self, seq_B: Tensor, seq_H: Tensor, scal: Tensor, T: Tensor, device) -> Tensor:\n",
    "\n",
    "        seq_B = seq_B.float()\n",
    "        seq_H = seq_H.float()\n",
    "        scal = scal.float()\n",
    "        T = T.float()\n",
    "\n",
    "        x_B, _ = self.lstm_B(seq_B)\n",
    "        x_B = x_B[:, -1, :]\n",
    "        x_H, _ = self.lstm_H(seq_H)\n",
    "        x_H = x_H[:, -1, :]\n",
    "\n",
    "        output = self.projector(torch.cat((scal, T, x_B, x_H), dim=1))\n",
    "        output = output.to(device)\n",
    "\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2CXI2TIOpNJl"
   },
   "source": [
    "# **Step 2: Load the Testing Dataset**\n",
    "\n",
    "The testing dataset consists of 105 sequences with varying past and future lengths, such as (900,100), (700,300), (500,500), (300,700), and (100,900). This function processes the raw data by interpolating and aligning the input sequences (B, H, T) to match the model's configuration. The model is designed to operate with a sampling rate of 16 MHz and a memory length of 80 time steps. Output H sequence is post processed again to provide autoregressive function. The user must specify the sampling frequency of the test data, which is 16 MHz in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(sample_freq_test, B, H, H_true, T, data_length, path_root):\n",
    "    \"\"\"\n",
    "    Prepares the test dataset by interpolating, aligning, and normalizing the inputs and outputs.\n",
    "\n",
    "    Args:\n",
    "        sample_freq_test (float): Sampling frequency of test data in Hz.\n",
    "        B (np.ndarray): Input B data including past and future sequences.\n",
    "        H (np.ndarray): Input magnetic field strength data representing the memory (past) sequence.\n",
    "        H_true (np.ndarray): Ground truth magnetic field strength including past and future values, used for evaluation.\n",
    "        T (np.ndarray): Temperature information.\n",
    "        data_length (int): Sequence length expected by the model.\n",
    "\n",
    "    Returns:\n",
    "        TensorDataset: Torch dataset containing (in_B, in_H, B_scal, T_scal, out)\n",
    "        normH (list): Normalization parameters for the output [mean_out, std_out].\n",
    "    \"\"\"\n",
    "\n",
    "    # Load normalization parameters from JSON\n",
    "    norm_file_name = 'Normalization_Params.json'\n",
    "    with open(path_root + norm_file_name, 'r') as f:\n",
    "        Param = json.load(f)\n",
    "\n",
    "    print(\"Normalization Initiated\")\n",
    "\n",
    "    # Extract normalization parameters\n",
    "    mean_B, std_B = np.array(Param['mean_B']), np.array(Param['std_B'])\n",
    "    mean_H, std_H = np.array(Param['mean_H']), np.array(Param['std_H'])\n",
    "    mean_out, std_out = np.array(Param['mean_out']), np.array(Param['std_out'])\n",
    "    mean_Scal, std_Scal = np.array(Param['mean_Scal']), np.array(Param['std_Scal'])\n",
    "    mean_T, std_T = np.array(Param['mean_T']), np.array(Param['std_T'])\n",
    "\n",
    "    # Get sequence lengths\n",
    "    B_len, H_len, H_true_len = B.shape[1], H.shape[1], H_true.shape[1]\n",
    "\n",
    "    print(\"B_length:\", B_len)\n",
    "    print(\"H_length:\", H_len)\n",
    "    print(\"H_true_length:\", H_true_len)\n",
    "\n",
    "    # Time calculations based on sampling frequency\n",
    "    sample_time_test = 1 / sample_freq_test\n",
    "    total_time_B = (B_len - 1) * sample_time_test\n",
    "    total_time_H = (H_len - 1) * sample_time_test\n",
    "    total_time_H_true = (H_true_len - 1) * sample_time_test\n",
    "\n",
    "    data_length_test = H_len\n",
    "    data_length_model = data_length\n",
    "\n",
    "    # Model's internal sampling parameters\n",
    "    sample_freq_model = 16e6\n",
    "    sample_time_model = 1 / sample_freq_model\n",
    "    time_factor = 100\n",
    "\n",
    "    # 1. Sampling frequency difference: Interpolation if sampling frequency mismatch exists\n",
    "    if sample_freq_model != sample_freq_test:\n",
    "        # Time axes for original and interpolated data\n",
    "        t_B_old = np.arange(B_len) * sample_time_test\n",
    "        t_H_old = np.arange(H_len) * sample_time_test\n",
    "        t_H_true_old = np.arange(H_true_len) * sample_time_test\n",
    "\n",
    "        t_B_new = np.arange(0, total_time_B, sample_time_model / time_factor)\n",
    "        t_H_new = np.arange(0, total_time_H, sample_time_model / time_factor)\n",
    "        t_H_true_new = np.arange(0, total_time_H_true, sample_time_model / time_factor)\n",
    "\n",
    "        # Interpolate each sample\n",
    "        B_inter = np.array([np.interp(t_B_new, t_B_old, B[i]) for i in range(B.shape[0])])\n",
    "        H_inter = np.array([np.interp(t_H_new, t_H_old, H[i]) for i in range(H.shape[0])])\n",
    "        H_true_inter = np.array([np.interp(t_H_true_new, t_H_true_old, H_true[i]) for i in range(H_true.shape[0])])\n",
    "\n",
    "        # Downsample after interpolation\n",
    "        B_seq = B_inter[:, ::time_factor]\n",
    "        H_seq = H_inter[:, ::time_factor]\n",
    "        H_true_seq = H_true_inter[:, ::time_factor]\n",
    "\n",
    "        data_length_test = H_seq.shape[1]\n",
    "        print(\"data_length_test:\", data_length_test)\n",
    "    else:\n",
    "        # If no interpolation is needed\n",
    "        B_seq, H_seq, H_true_seq = B, H, H_true\n",
    "\n",
    "    # 2. Time step difference: Adjust length of the sequence if different from model input\n",
    "    if data_length_model > data_length_test:\n",
    "        # Pad the front with the first value if test data is shorter\n",
    "        B_ftr = B_seq[:, data_length_test:]\n",
    "        H_ftr = H_true_seq[:, data_length_test:]\n",
    "\n",
    "        pad_len = data_length_model - data_length_test\n",
    "        B_seq = np.concatenate([np.tile(B_seq[:, [0]], (1, pad_len)), B_seq[:, :data_length_test]], axis=1)\n",
    "        H_seq = np.concatenate([np.tile(H_seq[:, [0]], (1, pad_len)), H_seq[:, :data_length_test]], axis=1)\n",
    "    else:\n",
    "        # Truncate if test data is longer\n",
    "        B_ftr = B_seq[:, data_length_test:]\n",
    "        H_ftr = H_true_seq[:, data_length_test:]\n",
    "        B_seq = B_seq[:, data_length_test - data_length_model:data_length_test]\n",
    "        H_seq = H_seq[:, data_length_test - data_length_model:data_length_test]\n",
    "\n",
    "    # Prepare input and output tensors\n",
    "    in_B = B_seq.copy()\n",
    "    in_H = H_seq.copy()\n",
    "    in_B_next = in_B.copy()\n",
    "\n",
    "    B_scal = B_ftr[:, [0]]\n",
    "    H_out = H_ftr[:, [0]]\n",
    "    T_scal = T.copy()\n",
    "\n",
    "    # Time-shift B_seq and append new values for sequence prediction\n",
    "    for i in range(B_ftr.shape[1] - 1):\n",
    "        in_B_next = np.roll(in_B_next, -1, axis=1)\n",
    "        in_B_next[:, -1] = B_ftr[:, i]\n",
    "\n",
    "        B_scal = np.vstack([B_scal, B_ftr[:, [i + 1]]])\n",
    "        H_out = np.vstack([H_out, H_ftr[:, [i + 1]]])\n",
    "        T_scal = np.vstack([T_scal, T])\n",
    "\n",
    "        in_B = np.vstack([in_B, in_B_next])\n",
    "        in_H = np.vstack([in_H, np.zeros_like(H_seq)])\n",
    "\n",
    "    # Normalize all inputs\n",
    "    B_scal = (B_scal - mean_Scal) / std_Scal\n",
    "    T_scal = (T_scal - mean_T) / std_T\n",
    "    in_B = (in_B - mean_B) / std_B\n",
    "    in_H = (in_H - mean_H) / std_H\n",
    "    out = (H_out - mean_out) / std_out\n",
    "\n",
    "    # Store normalization stats for inverse-transform\n",
    "    normH = [mean_out, std_out]\n",
    "\n",
    "    # Convert to PyTorch tensors\n",
    "    in_B = torch.from_numpy(in_B).float().view(-1, data_length_model, 1)\n",
    "    in_H = torch.from_numpy(in_H).float().view(-1, data_length_model, 1)\n",
    "    out = torch.from_numpy(out).float().view(-1, 1)\n",
    "    B_scal = torch.from_numpy(B_scal).float().view(-1, 1)\n",
    "    T_scal = torch.from_numpy(T_scal).float().view(-1, 1)\n",
    "\n",
    "    # Log shapes\n",
    "    print(\"in_B:\", in_B.size())\n",
    "    print(\"in_H:\", in_H.size())\n",
    "    print(\"B_scal:\", B_scal.size())\n",
    "    print(\"T_scal:\", T_scal.size())\n",
    "    print(\"out:\", out.size())\n",
    "\n",
    "    # # Optional plotting for visual check\n",
    "    # n_plot = 0\n",
    "\n",
    "    # plt.figure()\n",
    "    # plt.plot(B[n_plot].flatten())\n",
    "    # plt.title(\"Original B\") \n",
    "    # plt.xlabel(\"Index\")\n",
    "    # plt.ylabel(\"Value\")\n",
    "    # plt.grid(True)\n",
    "    # plt.show()\n",
    "\n",
    "    # plt.figure()\n",
    "    # plt.plot(H_true[n_plot].flatten())\n",
    "    # plt.title(\"Original H_true\")\n",
    "    # plt.xlabel(\"Index\")\n",
    "    # plt.ylabel(\"Value\")\n",
    "    # plt.grid(True)\n",
    "    # plt.show()\n",
    "\n",
    "    return torch.utils.data.TensorDataset(in_B, in_H, B_scal, T_scal, out), normH\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DJcX3ZPesurO"
   },
   "source": [
    "# **Step 3: Testing the Model**\n",
    "\n",
    "The loaded dataset is directly used as the test set. The model state dictionary file (.sd) containing all the trained parameter values is loaded and tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    \"\"\"Returns the number of trainable parameters in the model.\"\"\"\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "def compute_total_energy(B, H):\n",
    "    \"\"\"\n",
    "    Computes energy loss per unit volume [J/m³] for each sequence in B and H.\n",
    "    dB: [V·s/m²]\n",
    "    H: [A/m]\n",
    "    HdB: [J/m³]\n",
    "    Open loop integration: Estimate of energy exchanged (not total core loss) — includes both loss and stored energy.\n",
    "    B, H: numpy arrays of shape (N_sequences, N_timesteps)\n",
    "    Returns: energy loss per sequence (N_sequences,)\n",
    "    \"\"\"\n",
    "    dB = np.diff(B, axis=1)                      # shape: (N, T-1)\n",
    "    H_mid = (H[:, :-1] + H[:, 1:]) / 2           # shape: (N, T-1)\n",
    "    energy_density = np.sum(H_mid * dB, axis=1)  # shape: (N,)\n",
    "    return np.abs(energy_density)                # abs to ensure positive energy\n",
    "\n",
    "\n",
    "def Test(sample_freq_test, B, H, H_true, T, data_length, path_root):\n",
    "\n",
    "    # Identify index of NaNs in H to determine future prediction boundaries\n",
    "    idx_nan = np.argmax(np.isnan(H), axis=1)\n",
    "    has_nan = np.any(np.isnan(H), axis=1)\n",
    "    idx_nan[~has_nan] = -1\n",
    "\n",
    "    # Segment the dataset based on changes in NaN positions\n",
    "    lgt_change = np.where(np.diff(idx_nan))[0] + 1\n",
    "    segment_start = np.insert(lgt_change, 0, 0)\n",
    "    segment_stop = np.append(lgt_change, len(idx_nan))\n",
    "    \n",
    "    all_y_pred = []\n",
    "    all_y_meas = []\n",
    "    idx_st_list = []\n",
    "\n",
    "    for i, (start, stop) in enumerate(zip(segment_start, segment_stop)):\n",
    "        idx_st = idx_nan[start]\n",
    "\n",
    "        B_seq = B[start:stop, :]\n",
    "        H_mem = H[start:stop, :idx_st]\n",
    "        T_scal = T[start:stop, :]\n",
    "        H_meas = H_true[start:stop, :]\n",
    "\n",
    "        BATCH_SIZE = len(B_seq)\n",
    "\n",
    "        # Load dataset\n",
    "        dataset, normH = get_dataset(sample_freq_test, B_seq, H_mem, H_meas, T_scal, data_length, path_root)\n",
    "        print(f\"Data Iteration {i + 1} - dataset shape: {dataset.tensors[1].shape}\")\n",
    "\n",
    "        # Set random seed for reproducibility\n",
    "        random.seed(1)\n",
    "        np.random.seed(1)\n",
    "        torch.manual_seed(1)\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "\n",
    "        # Use CUDA\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        # device = torch.device(\"cuda\")\n",
    "\n",
    "        # Create DataLoader\n",
    "        test_loader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False,\n",
    "                                                  num_workers=0, pin_memory=True, pin_memory_device=\"cuda\")\n",
    "        test_data = list(test_loader)\n",
    "\n",
    "        # Load model\n",
    "        net = Net().to(device)\n",
    "        print(\"Number of parameters:\", count_parameters(net))\n",
    "\n",
    "        state_dict = torch.load(path_root + 'Model_LSTM.sd')\n",
    "        net.load_state_dict(state_dict, strict=True)\n",
    "        net.eval()\n",
    "        print(\"Model loaded successfully.\")\n",
    "\n",
    "        # Containers for predictions\n",
    "        out_pred = torch.tensor([]).to(device)\n",
    "        out_meas = torch.tensor([]).to(device)\n",
    "        previous_in_H = None\n",
    "\n",
    "\n",
    "        print(\"Normalization - Mean:\", normH[0], \"Std:\", normH[1])\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for in_B, in_H, B_scal, T_scal, out in test_data:\n",
    "                in_B, in_H = in_B.to(device), in_H.to(device)\n",
    "                B_scal, T_scal, out = B_scal.to(device), T_scal.to(device), out.to(device)\n",
    "\n",
    "                if previous_in_H is None:\n",
    "                    outputs = net(seq_B=in_B, seq_H=in_H, scal=B_scal, T=T_scal, device=device)\n",
    "                else:\n",
    "                    # Update in_H autoregressively with previous prediction\n",
    "                    in_H = np.roll(previous_in_H.cpu(), -1, axis=1)\n",
    "                    in_H[:, -1] = out_pred[:, -1].cpu().numpy().reshape(-1, 1)\n",
    "                    in_H = torch.from_numpy(in_H).float().to(device)\n",
    "                    outputs = net(seq_B=in_B, seq_H=in_H, scal=B_scal, T=T_scal, device=device)\n",
    "\n",
    "                # Store predictions and targets\n",
    "                out_pred = torch.cat((out_pred, outputs), dim=1)\n",
    "                out_meas = torch.cat((out_meas, out), dim=1)\n",
    "                previous_in_H = in_H\n",
    "\n",
    "                # Optional debugging output\n",
    "                # print(\"Predicted:\", outputs[0].cpu().numpy(), \"Measured:\", out[0].cpu().numpy())\n",
    "\n",
    "            # Denormalize results\n",
    "            y_meas = out_meas.cpu().numpy() * normH[1] + normH[0]\n",
    "            y_pred = out_pred.cpu().numpy() * normH[1] + normH[0]\n",
    "\n",
    "            # Save to CSV\n",
    "            with open(path_root + \"Output/pred.csv\", \"a\") as f:\n",
    "                np.savetxt(f, y_pred, delimiter=',')\n",
    "            with open(path_root + \"Output/meas.csv\", \"a\") as f:\n",
    "                np.savetxt(f, y_meas, delimiter=',')\n",
    "\n",
    "            all_y_meas.append(np.concatenate([H[start:stop,:idx_st], y_meas], axis=1))\n",
    "            all_y_pred.append(np.concatenate([H[start:stop,:idx_st], y_pred], axis=1))   \n",
    "            idx_st_list.extend([idx_st] * (stop - start))\n",
    "            print(\"Testing finished and results saved.\\n\")\n",
    "\n",
    "    print(\"All test segments processed.\")\n",
    "\n",
    "    all_y_pred = np.concatenate(all_y_pred, axis=0)\n",
    "    all_y_meas = np.concatenate(all_y_meas, axis=0)\n",
    "\n",
    "    return all_y_pred, all_y_meas, idx_st_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 4: Save Outputs**\n",
    "\n",
    "After completing the inference, the predicted and measured H sequences are denormalized and saved as CSV files for further analysis. These outputs are appended for each test segment, preserving both the memory input and predicted values in sequence. The resulting files (pred.csv and meas.csv) can be used for evaluation, plotting, or model performance comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys in padded file: ['B_seq', 'H_seq', 'T']\n",
      "Test Data Loaded Successfully\n",
      "Keys in true file: ['B_seq', 'H_seq', 'T']\n",
      "Ground Truth Data Loaded Successfully\n",
      "Normalization Initiated\n",
      "B_length: 1000\n",
      "H_length: 100\n",
      "H_true_length: 1000\n",
      "in_B: torch.Size([18900, 80, 1])\n",
      "in_H: torch.Size([18900, 80, 1])\n",
      "B_scal: torch.Size([18900, 1])\n",
      "T_scal: torch.Size([18900, 1])\n",
      "out: torch.Size([18900, 1])\n",
      "Data Iteration 1 - dataset shape: torch.Size([18900, 80, 1])\n",
      "Number of parameters: 2399\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/Users/kwonhyukjae/Princeton Dropbox/Hyukjae Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb 셀 10\u001b[0m line \u001b[0;36m3\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m sample_freq_test \u001b[39m=\u001b[39m \u001b[39m16e6\u001b[39m           \u001b[39m# Sampling frequency (Hz)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39m# Run your testing function with loaded data\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m y_pred, y_meas, idx_st_list \u001b[39m=\u001b[39m Test(sample_freq_test, B, H, H_true, T_scal, data_length, path_root)\n",
      "\u001b[1;32m/Users/kwonhyukjae/Princeton Dropbox/Hyukjae Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb 셀 10\u001b[0m line \u001b[0;36m6\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m net \u001b[39m=\u001b[39m Net()\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=64'>65</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mNumber of parameters:\u001b[39m\u001b[39m\"\u001b[39m, count_parameters(net))\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m state_dict \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mload(path_root \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39mModel_LSTM.sd\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=67'>68</a>\u001b[0m net\u001b[39m.\u001b[39mload_state_dict(state_dict, strict\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/kwonhyukjae/Princeton%20Dropbox/Hyukjae%20Kwon/MLTran/tutorial-3/Demo_LSTM_Evaluation.ipynb#X13sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m net\u001b[39m.\u001b[39meval()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:1516\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1514\u001b[0m \u001b[39mif\u001b[39;00m weights_only:\n\u001b[1;32m   1515\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1516\u001b[0m         \u001b[39mreturn\u001b[39;00m _load(\n\u001b[1;32m   1517\u001b[0m             opened_zipfile,\n\u001b[1;32m   1518\u001b[0m             map_location,\n\u001b[1;32m   1519\u001b[0m             _weights_only_unpickler,\n\u001b[1;32m   1520\u001b[0m             overall_storage\u001b[39m=\u001b[39moverall_storage,\n\u001b[1;32m   1521\u001b[0m             \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpickle_load_args,\n\u001b[1;32m   1522\u001b[0m         )\n\u001b[1;32m   1523\u001b[0m     \u001b[39mexcept\u001b[39;00m pickle\u001b[39m.\u001b[39mUnpicklingError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   1524\u001b[0m         \u001b[39mraise\u001b[39;00m pickle\u001b[39m.\u001b[39mUnpicklingError(_get_wo_message(\u001b[39mstr\u001b[39m(e))) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:2114\u001b[0m, in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[1;32m   2112\u001b[0m \u001b[39mglobal\u001b[39;00m _serialization_tls\n\u001b[1;32m   2113\u001b[0m _serialization_tls\u001b[39m.\u001b[39mmap_location \u001b[39m=\u001b[39m map_location\n\u001b[0;32m-> 2114\u001b[0m result \u001b[39m=\u001b[39m unpickler\u001b[39m.\u001b[39mload()\n\u001b[1;32m   2115\u001b[0m _serialization_tls\u001b[39m.\u001b[39mmap_location \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   2117\u001b[0m torch\u001b[39m.\u001b[39m_utils\u001b[39m.\u001b[39m_validate_loaded_sparse_tensors()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/_weights_only_unpickler.py:532\u001b[0m, in \u001b[0;36mUnpickler.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    524\u001b[0m     \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    525\u001b[0m         \u001b[39mtype\u001b[39m(pid) \u001b[39mis\u001b[39;00m \u001b[39mtuple\u001b[39m\n\u001b[1;32m    526\u001b[0m         \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(pid) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m    527\u001b[0m         \u001b[39mand\u001b[39;00m torch\u001b[39m.\u001b[39mserialization\u001b[39m.\u001b[39m_maybe_decode_ascii(pid[\u001b[39m0\u001b[39m]) \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mstorage\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m     ):\n\u001b[1;32m    529\u001b[0m         \u001b[39mraise\u001b[39;00m UnpicklingError(\n\u001b[1;32m    530\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mOnly persistent_load of storage is allowed, but got \u001b[39m\u001b[39m{\u001b[39;00mpid[\u001b[39m0\u001b[39m]\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    531\u001b[0m         )\n\u001b[0;32m--> 532\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpersistent_load(pid))\n\u001b[1;32m    533\u001b[0m \u001b[39melif\u001b[39;00m key[\u001b[39m0\u001b[39m] \u001b[39min\u001b[39;00m [BINGET[\u001b[39m0\u001b[39m], LONG_BINGET[\u001b[39m0\u001b[39m]]:\n\u001b[1;32m    534\u001b[0m     idx \u001b[39m=\u001b[39m (read(\u001b[39m1\u001b[39m) \u001b[39mif\u001b[39;00m key[\u001b[39m0\u001b[39m] \u001b[39m==\u001b[39m BINGET[\u001b[39m0\u001b[39m] \u001b[39melse\u001b[39;00m unpack(\u001b[39m\"\u001b[39m\u001b[39m<I\u001b[39m\u001b[39m\"\u001b[39m, read(\u001b[39m4\u001b[39m)))[\u001b[39m0\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:2078\u001b[0m, in \u001b[0;36m_load.<locals>.persistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m   2076\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2077\u001b[0m     nbytes \u001b[39m=\u001b[39m numel \u001b[39m*\u001b[39m torch\u001b[39m.\u001b[39m_utils\u001b[39m.\u001b[39m_element_size(dtype)\n\u001b[0;32m-> 2078\u001b[0m     typed_storage \u001b[39m=\u001b[39m load_tensor(\n\u001b[1;32m   2079\u001b[0m         dtype, nbytes, key, _maybe_decode_ascii(location)\n\u001b[1;32m   2080\u001b[0m     )\n\u001b[1;32m   2082\u001b[0m \u001b[39mreturn\u001b[39;00m typed_storage\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:2044\u001b[0m, in \u001b[0;36m_load.<locals>.load_tensor\u001b[0;34m(dtype, numel, key, location)\u001b[0m\n\u001b[1;32m   2040\u001b[0m \u001b[39m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[1;32m   2041\u001b[0m \u001b[39m# stop wrapping with TypedStorage\u001b[39;00m\n\u001b[1;32m   2043\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_guards\u001b[39m.\u001b[39mdetect_fake_mode(\u001b[39mNone\u001b[39;00m) \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 2044\u001b[0m     wrap_storage \u001b[39m=\u001b[39m restore_location(storage, location)\n\u001b[1;32m   2045\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2046\u001b[0m     storage\u001b[39m.\u001b[39m_fake_device \u001b[39m=\u001b[39m location\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:698\u001b[0m, in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    679\u001b[0m \u001b[39mRestores `storage` using a deserializer function registered for the `location`.\u001b[39;00m\n\u001b[1;32m    680\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    695\u001b[0m \u001b[39m       all matching ones return `None`.\u001b[39;00m\n\u001b[1;32m    696\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    697\u001b[0m \u001b[39mfor\u001b[39;00m _, _, fn \u001b[39min\u001b[39;00m _package_registry:\n\u001b[0;32m--> 698\u001b[0m     result \u001b[39m=\u001b[39m fn(storage, location)\n\u001b[1;32m    699\u001b[0m     \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    700\u001b[0m         \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:636\u001b[0m, in \u001b[0;36m_deserialize\u001b[0;34m(backend_name, obj, location)\u001b[0m\n\u001b[1;32m    634\u001b[0m     backend_name \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_get_privateuse1_backend_name()\n\u001b[1;32m    635\u001b[0m \u001b[39mif\u001b[39;00m location\u001b[39m.\u001b[39mstartswith(backend_name):\n\u001b[0;32m--> 636\u001b[0m     device \u001b[39m=\u001b[39m _validate_device(location, backend_name)\n\u001b[1;32m    637\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39mto(device\u001b[39m=\u001b[39mdevice)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/serialization.py:605\u001b[0m, in \u001b[0;36m_validate_device\u001b[0;34m(location, backend_name)\u001b[0m\n\u001b[1;32m    603\u001b[0m     device_index \u001b[39m=\u001b[39m device\u001b[39m.\u001b[39mindex \u001b[39mif\u001b[39;00m device\u001b[39m.\u001b[39mindex \u001b[39melse\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[1;32m    604\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(device_module, \u001b[39m\"\u001b[39m\u001b[39mis_available\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m device_module\u001b[39m.\u001b[39mis_available():\n\u001b[0;32m--> 605\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    606\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mAttempting to deserialize object on a \u001b[39m\u001b[39m{\u001b[39;00mbackend_name\u001b[39m.\u001b[39mupper()\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    607\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mdevice but torch.\u001b[39m\u001b[39m{\u001b[39;00mbackend_name\u001b[39m}\u001b[39;00m\u001b[39m.is_available() is False. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    608\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mIf you are running on a CPU-only machine, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    609\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m'\u001b[39m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    610\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mto map your storages to the CPU.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    611\u001b[0m     )\n\u001b[1;32m    612\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(device_module, \u001b[39m\"\u001b[39m\u001b[39mdevice_count\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m    613\u001b[0m     device_count \u001b[39m=\u001b[39m device_module\u001b[39m.\u001b[39mdevice_count()\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU."
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    # Define the root path and filenames\n",
    "    path_root = 'C:/Users/Labadmin/Princeton Dropbox/Hyukjae Kwon/MLTran/tutorial-3/'\n",
    "    file_name = '3C90_Testing_padded.h5'\n",
    "    file_name_true = '3C90_Testing_true.h5'\n",
    "\n",
    "    # --- Load Test Data from HDF5 File ---\n",
    "    with h5py.File(path_root + file_name, 'r') as file:\n",
    "        print(\"Keys in padded file:\", list(file.keys()))\n",
    "\n",
    "        B = file['B_seq'][:]\n",
    "        H = file['H_seq'][:]\n",
    "        T_scal = file['T'][:]\n",
    "\n",
    "    print(\"Test Data Loaded Successfully\")\n",
    "\n",
    "    # --- Load Ground Truth Data from HDF5 File ---\n",
    "    with h5py.File(path_root + file_name_true, 'r') as file:\n",
    "        print(\"Keys in true file:\", list(file.keys()))\n",
    "\n",
    "        H_true = file['H_seq'][:]\n",
    "\n",
    "    print(\"Ground Truth Data Loaded Successfully\")\n",
    "\n",
    "    # --- Set Parameters and Run Test Function ---\n",
    "    data_length = 80                  # Length of each data sequence\n",
    "    sample_freq_test = 16e6           # Sampling frequency (Hz)\n",
    "\n",
    "    # Run your testing function with loaded data\n",
    "    y_pred, y_meas, idx_st_list = Test(sample_freq_test, B, H, H_true, T_scal, data_length, path_root)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Visualization of Prediction Results**\n",
    "\n",
    "This section illustrates the predicted and measured magnetic field sequences, as well as energy loss computed from B-H loops. NRMSE is reported to assess accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot one example (memory + prediction)\n",
    "n_plot = 80\n",
    "mem_len = idx_st_list[n_plot]\n",
    "pred_len = y_pred.shape[1] - mem_len\n",
    "\n",
    "in_H_plot = y_pred[n_plot, :mem_len]           # memory input\n",
    "pred_plot = y_pred[n_plot, mem_len:]           # prediction\n",
    "meas_plot = y_meas[n_plot, mem_len:]           # ground truth\n",
    "\n",
    "plt.figure(figsize=(14, 5))\n",
    "plt.plot(range(mem_len), in_H_plot, label='H (memory)', linestyle=':', color='gray')\n",
    "plt.plot(range(mem_len, mem_len + pred_len), pred_plot, label='Predicted', color='r')\n",
    "plt.plot(range(mem_len, mem_len + pred_len), meas_plot, label='Measured', linestyle='--', color='b')\n",
    "\n",
    "plt.axvline(x=mem_len - 1, color='k', linestyle='--')  # prediction start\n",
    "plt.text(mem_len - 1, plt.ylim()[1]*0.95, 'Prediction Start', rotation=90, va='top', ha='right')\n",
    "\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"H (A/m)\")\n",
    "plt.title(f\"Sample #{n_plot}: H memory + Predicted vs Measured\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean NRMSE across all sequences: 16.13%\n",
      "Memory length 100: Mean NRMSE = 18.74% (n = 21)\n",
      "Memory length 300: Mean NRMSE = 20.67% (n = 21)\n",
      "Memory length 500: Mean NRMSE = 12.25% (n = 21)\n",
      "Memory length 700: Mean NRMSE = 15.57% (n = 21)\n",
      "Memory length 900: Mean NRMSE = 13.40% (n = 21)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Labadmin\\AppData\\Local\\Temp\\ipykernel_45140\\2742749261.py:65: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute NRMSE (%) for each sequence\n",
    "nrmse_list = []\n",
    "nrmse_dict = defaultdict(list)\n",
    "for i in range(len(y_pred)):\n",
    "    mem_len = idx_st_list[i]\n",
    "    pred = y_pred[i, mem_len:]\n",
    "    meas = y_meas[i, mem_len:]\n",
    "\n",
    "    mse = np.mean((pred - meas) ** 2)\n",
    "    rmse = np.sqrt(mse)\n",
    "    norm = np.mean(np.abs(meas)) if np.mean(np.abs(meas)) != 0 else 1e-8  # avoid division by zero\n",
    "    nrmse = (rmse / norm) * 100  # convert to percentage\n",
    "    nrmse_list.append(nrmse)\n",
    "    nrmse_dict[mem_len].append(nrmse)\n",
    "\n",
    "nrmse_array = np.array(nrmse_list)\n",
    "\n",
    "# Plot NRMSE as percentage\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(nrmse_array, marker='o', linestyle='-', color='purple')\n",
    "plt.xlabel(\"Sequence Index\")\n",
    "plt.ylabel(\"NRMSE (%)\")\n",
    "plt.title(\"Normalized Root Mean Squared Error (NRMSE %) for Each Sequence\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print mean NRMSE in percentage\n",
    "mean_nrmse = np.mean(nrmse_array)\n",
    "print(f\"Mean NRMSE across all sequences: {mean_nrmse:.2f}%\")\n",
    "\n",
    "for mem_len, values in sorted(nrmse_dict.items()):\n",
    "    mean_nrmse = np.mean(values)\n",
    "    print(f\"Memory length {mem_len}: Mean NRMSE = {mean_nrmse:.2f}% (n = {len(values)})\")\n",
    "\n",
    "   \n",
    "# Plot NRMSE Histogram\n",
    "# Calculate statistics\n",
    "mean_error = np.mean(np.abs(nrmse_list))\n",
    "percentile_95 = np.percentile(np.abs(nrmse_list), 95)\n",
    "\n",
    "# Plot histogram\n",
    "plt.figure(figsize=(6, 5))\n",
    "counts, bins, patches = plt.hist(np.abs(nrmse_list), bins=30, color=(0, 0.4470, 0.7410), alpha=1.0,\n",
    "                                  edgecolor='black', density=True)\n",
    "\n",
    "# Draw vertical lines for mean and 95th percentile\n",
    "y_max = max(counts) * 1.1\n",
    "y = np.linspace(0, y_max, 100)\n",
    "\n",
    "plt.plot([mean_error]*len(y), y, '--', color='red', linewidth=2)\n",
    "plt.plot([percentile_95]*len(y), y, '--', color='green', linewidth=2)\n",
    "\n",
    "# Text annotations\n",
    "plt.text(mean_error, y_max*0.9, f'  Ave Error = {mean_error:.2f}%', color='red', ha='left')\n",
    "plt.text(percentile_95, y_max*0.8, f'  95-Prct = {percentile_95:.2f}%', color='green', ha='left')\n",
    "\n",
    "# Plot formatting\n",
    "plt.xlabel(\"Relative Error of the Sequence (%)\")\n",
    "plt.ylabel(\"Ratio of Data Points\")\n",
    "plt.title(\"Histogram of Normalized RMSE (NRMSE %)\")\n",
    "plt.grid(True)\n",
    "plt.xlim([0, 80])\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NRMSE: 39.77666323876542 %\n"
     ]
    }
   ],
   "source": [
    "# Plot predicted vs. measured energy loss and NRMSE for 105 sequences of 1,000 steps\n",
    "\n",
    "energy_pred = compute_total_energy(B, y_pred)\n",
    "energy_meas = compute_total_energy(B, y_meas)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(energy_pred, label='Predicted Energy', marker='o')\n",
    "plt.plot(energy_meas, label='Measured Energy', marker='x')\n",
    "plt.xlabel(\"Sequence Index\")\n",
    "plt.ylabel(\"Energy Loss per Volume (J/m³)\")\n",
    "plt.title(\"Energy Loss Computed from B-H Loops\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('NRMSE:', np.sqrt(np.mean((energy_pred - energy_meas) ** 2)) / np.mean(np.abs(energy_meas)) * 100,'%')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyMUtNOJpB+v1CYXAsgvlAWC",
   "gpuType": "T4",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "torch-gpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
